Making Systems Fail-Aware: A Semi-Supervised Machine Learning Approach for Identifying Failures by Learning the Correct Behavior of a System

Abstract

Observing the interaction between a system, its environment, and its internal state is vital to detect failures during operation. Monitoring systems often use predefined system properties to detect such failures, and violations indicate potential failures. However, obtaining these properties is work-intensive and error-prone. Therefore, we describe an approach to obtain a system model by learning only the correct behavior using machine learning. Monitoring systems can use such models to predict correct future behavior. A potential failure is raised if real-world data deviate significantly from this prediction. We use a semi-supervised LSTM-based forecasting approach with a simple architecture, apply our approach to simulation data from a battery control system, and discuss our experimental results.